一种自适应联邦滤波在车载组合导航中的应用  

Adaptive federated filter and its application to integrated navigation system of land vehicle.

作　　者：贺娟[1] 张芳芳[2] 贺晓蓉[1] 

机构地区：[1]重庆工学院电子信息与自动化学院,重庆400050 [2]滨州学院自动化系,山东滨州256600

出　　处：《计算机工程与应用》2008年第7期238-241,共4页Computer Engineering and Applications

摘　　要：联邦滤波在组合导航的应用中,具有容错性好、滤波精度高、计算量小以及实时性好的特点,但在无法得到准确的系统模型时,使用联邦滤波会出现滤波精度低甚至发散的情况。针对车载组合导航信息融合的高精度、高可靠性等要求,提出了一种组合导航的自适应联邦滤波算法。其主要思想是以判别观测数据中的野值存在与否为算法切换条件,存在野值时采用改进的增益矩阵滤波处理方法,不存在野值时则采用模糊自适应联邦滤波方法。将此方法用于SINS/GPS车载组合导航系统中,实验表明,采用的这种自适应滤波方法,能够有效抑制滤波发散,其滤波精度和收敛速度要优于常规联邦滤波,是一种有效的车载组合导航算法。Federated Kalman filter is always use to integrated navigation system to achieve anti-jamming,high guidanceprecision,small amount and real-time filter.Nevertheless,the precision of federated Kalman filter depends on the accurate degree of system model.A new adaptive federated filter used in integrated navigation system of land vehicle is presented to achieve the high guidance-precision and anti-jamming.The main idea is using the fault datum as the switching condition.While fault datum exists,we choose improving gain matrix of the Kalman filter approach;else we choose a fuzzy adaptive Kalman filter approach. Experiment results show that,when this approach uses to SINS/GPS integrated navigation systems,filtering precision and converging speed is over general Kalman filter.

关 键 词：车载组合导航 信息融合 模糊推理 联邦滤波 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]